This notebook contains a set of analyses for analyzing rahdo’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
rahdo | training | published before 2020 | 1,240 | 307 |
rahdo | validation | published 2020 | 109 | 40 |
rahdo | test | published after 2020 | 121 | 53 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
rahdo | Artist Klemens Franz | 5.2% | 0.3% | 16.06 |
rahdo | Renegade Game Studios | 3.2% | 0.2% | 14.34 |
rahdo | Stronghold Games | 4.4% | 0.4% | 12.32 |
rahdo | Worker Placement | 18.9% | 2.3% | 8.10 |
rahdo | City Building | 12.3% | 1.7% | 7.45 |
rahdo | Rio Grande Games | 8.6% | 1.5% | 5.82 |
rahdo | Economic | 21.2% | 6.0% | 3.52 |
rahdo | Set Collection | 31.9% | 12.1% | 2.63 |
rahdo | Dice Rolling | 24.3% | 28.7% | 0.85 |
rahdo | Take That | 4.3% | 5.2% | 0.82 |
rahdo | Miniatures Game | 2.8% | 4.9% | 0.57 |
rahdo | Abstract Strategy | 3.4% | 7.5% | 0.45 |
rahdo | Negotiation | 1.0% | 3.3% | 0.32 |
rahdo | Roll Spin And Move | 0.8% | 7.1% | 0.11 |
rahdo | Childrens Game | 0.4% | 8.5% | 0.05 |
rahdo | Wargame | 0.7% | 19.9% | 0.04 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2008 | 35677 | Le Havre | 0.995 | yes |
2 | 2015 | 175878 | 504 | 0.995 | yes |
3 | 2010 | 70512 | Luna | 0.995 | yes |
4 | 2013 | 143693 | Glass Road | 0.994 | yes |
5 | 2011 | 70149 | Ora et Labora | 0.993 | yes |
6 | 2018 | 244049 | Forum Trajanum | 0.992 | yes |
7 | 2019 | 286096 | Tapestry | 0.990 | yes |
8 | 2009 | 39683 | At the Gates of Loyang | 0.987 | yes |
9 | 2016 | 167791 | Terraforming Mars | 0.986 | yes |
10 | 2014 | 159508 | AquaSphere | 0.983 | yes |
11 | 2007 | 31260 | Agricola | 0.980 | yes |
12 | 2016 | 200680 | Agricola (Revised Edition) | 0.979 | no |
13 | 2015 | 172385 | Porta Nigra | 0.979 | yes |
14 | 2017 | 230933 | Merlin | 0.978 | yes |
15 | 2010 | 73439 | Troyes | 0.977 | yes |
16 | 2019 | 283863 | The Magnificent | 0.977 | yes |
17 | 2018 | 241831 | Reykholt | 0.976 | yes |
18 | 2016 | 177736 | A Feast for Odin | 0.975 | yes |
19 | 2018 | 199792 | Everdell | 0.974 | no |
20 | 2014 | 159675 | Fields of Arle | 0.974 | yes |
21 | 2018 | 244711 | Newton | 0.971 | yes |
22 | 2016 | 192945 | Coal Baron: The Great Card Game | 0.971 | yes |
23 | 2017 | 234487 | Altiplano | 0.971 | yes |
24 | 2019 | 271320 | The Castles of Burgundy | 0.971 | no |
25 | 2011 | 102680 | Trajan | 0.970 | yes |
26 | 2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.968 | no |
27 | 2008 | 38453 | Space Alert | 0.968 | yes |
28 | 2012 | 123260 | Suburbia | 0.967 | yes |
29 | 2017 | 232988 | The Castles of Burgundy: The Dice Game | 0.967 | yes |
30 | 2017 | 233676 | Noria | 0.967 | yes |
31 | 2010 | 66505 | The Speicherstadt | 0.963 | yes |
32 | 2019 | 272682 | Expedition to Newdale | 0.962 | yes |
33 | 2012 | 122515 | Keyflower | 0.962 | yes |
34 | 2011 | 84876 | The Castles of Burgundy | 0.960 | yes |
35 | 2013 | 143515 | Coal Baron | 0.960 | yes |
36 | 2007 | 27173 | Vikings | 0.960 | yes |
37 | 2019 | 270971 | Era: Medieval Age | 0.960 | yes |
38 | 2008 | 34635 | Stone Age | 0.959 | yes |
39 | 2018 | 214887 | CO₂: Second Chance | 0.959 | yes |
40 | 2013 | 137408 | Amerigo | 0.957 | yes |
41 | 2014 | 157354 | Five Tribes | 0.956 | yes |
42 | 2012 | 119890 | Agricola: All Creatures Big and Small | 0.952 | yes |
43 | 2018 | 245934 | Carpe Diem | 0.951 | yes |
44 | 2013 | 102794 | Caverna: The Cave Farmers | 0.947 | yes |
45 | 2019 | 257066 | Sierra West | 0.945 | yes |
46 | 2017 | 220308 | Gaia Project | 0.945 | yes |
47 | 2007 | 31594 | In the Year of the Dragon | 0.944 | yes |
48 | 2016 | 193558 | The Oracle of Delphi | 0.944 | yes |
49 | 2014 | 157809 | Nations: The Dice Game | 0.943 | yes |
50 | 2017 | 229265 | Wendake | 0.943 | no |
51 | 2014 | 164928 | Orléans | 0.942 | yes |
52 | 2017 | 201825 | Ex Libris | 0.942 | yes |
53 | 2019 | 266507 | Clank!: Legacy – Acquisitions Incorporated | 0.941 | yes |
54 | 2016 | 193739 | Jórvík | 0.941 | yes |
55 | 2011 | 91873 | Strasbourg | 0.939 | yes |
56 | 2017 | 227789 | Heaven & Ale | 0.936 | yes |
57 | 2015 | 183394 | Viticulture Essential Edition | 0.933 | no |
58 | 2013 | 136888 | Bruges | 0.931 | yes |
59 | 2018 | 247763 | Underwater Cities | 0.931 | yes |
60 | 2011 | 70919 | Takenoko | 0.930 | yes |
61 | 2013 | 124361 | Concordia | 0.930 | yes |
62 | 2015 | 172386 | Mombasa | 0.929 | yes |
63 | 2019 | 284435 | Nova Luna | 0.927 | no |
64 | 2017 | 227515 | Riverboat | 0.926 | yes |
65 | 2007 | 28143 | Race for the Galaxy | 0.925 | yes |
66 | 2018 | 236457 | Architects of the West Kingdom | 0.924 | yes |
67 | 2017 | 162886 | Spirit Island | 0.924 | yes |
68 | 2017 | 199383 | Calimala | 0.923 | yes |
69 | 2016 | 191977 | The Castles of Burgundy: The Card Game | 0.923 | yes |
70 | 2009 | 55670 | Macao | 0.923 | yes |
71 | 2018 | 223514 | Coin & Crown | 0.921 | no |
72 | 2014 | 150926 | Roll Through the Ages: The Iron Age | 0.920 | no |
73 | 2011 | 90041 | Principato | 0.919 | yes |
74 | 2014 | 146886 | La Granja | 0.918 | yes |
75 | 2007 | 25554 | Notre Dame | 0.916 | yes |
76 | 2017 | 229220 | Santa Maria | 0.914 | yes |
77 | 2018 | 240464 | Cosmic Run: Regeneration | 0.912 | yes |
78 | 2019 | 264052 | Circadians: First Light | 0.909 | yes |
79 | 2017 | 218920 | Valletta | 0.909 | yes |
80 | 2019 | 253635 | Ragusa | 0.909 | yes |
81 | 2016 | 187653 | Covert | 0.907 | yes |
82 | 2013 | 140620 | Lewis & Clark: The Expedition | 0.907 | yes |
83 | 2018 | 260428 | Pandemic: Fall of Rome | 0.904 | no |
84 | 2011 | 104006 | Village | 0.903 | yes |
85 | 2016 | 176371 | Explorers of the North Sea | 0.903 | no |
86 | 2013 | 52461 | Legacy: The Testament of Duke de Crecy | 0.899 | yes |
87 | 2018 | 256916 | Concordia Venus | 0.897 | no |
88 | 2014 | 132531 | Roll for the Galaxy | 0.896 | yes |
89 | 2017 | 220520 | Caverna: Cave vs Cave | 0.894 | yes |
90 | 2018 | 245638 | Coimbra | 0.892 | yes |
91 | 2017 | 223953 | Kitchen Rush | 0.892 | no |
92 | 2013 | 144344 | Rococo | 0.892 | yes |
93 | 2017 | 221194 | Dinosaur Island | 0.892 | yes |
94 | 2017 | 193031 | Coal Country | 0.891 | no |
95 | 2018 | 231581 | AuZtralia | 0.891 | no |
96 | 2018 | 257966 | Passing Through Petra | 0.890 | yes |
97 | 2017 | 214000 | In the Year of the Dragon: 10th Anniversary | 0.890 | no |
98 | 2012 | 117915 | Yedo | 0.888 | yes |
99 | 2016 | 193738 | Great Western Trail | 0.886 | yes |
100 | 2007 | 31481 | Galaxy Trucker | 0.884 | yes |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.93 |
Decision Tree | roc_auc | binary | 0.86 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think rahdo is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2016 | 200680 | Agricola (Revised Edition) | 0.979 | no |
2018 | 199792 | Everdell | 0.974 | no |
2019 | 271320 | The Castles of Burgundy | 0.971 | no |
2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.968 | no |
2017 | 229265 | Wendake | 0.943 | no |
What games does the model think rahdo is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2013 | 139771 | Star Trek: Attack Wing | 0.001 | yes |
2017 | 180845 | ELL deck | 0.003 | yes |
2011 | 93538 | Battleship Galaxies | 0.006 | yes |
1983 | 438 | Scotland Yard | 0.007 | yes |
2005 | 12333 | Twilight Struggle | 0.007 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Robinson Crusoe: Adventures on the Cursed Island | Glass Road | AquaSphere | 504 | Terraforming Mars | Merlin | Forum Trajanum | Tapestry |
2 | Suburbia | Coal Baron | Fields of Arle | Porta Nigra | Agricola (Revised Edition) | Altiplano | Reykholt | The Magnificent |
3 | Keyflower | Amerigo | Five Tribes | Viticulture Essential Edition | A Feast for Odin | The Castles of Burgundy: The Dice Game | Everdell | The Castles of Burgundy |
4 | Agricola: All Creatures Big and Small | Caverna: The Cave Farmers | Nations: The Dice Game | Mombasa | Coal Baron: The Great Card Game | Noria | Newton | Expedition to Newdale |
5 | Yedo | Bruges | Orléans | My Village | The Oracle of Delphi | Gaia Project | CO₂: Second Chance | Era: Medieval Age |
6 | Escape: The Curse of the Temple | Concordia | Roll Through the Ages: The Iron Age | Harbour | Jórvík | Wendake | Carpe Diem | Sierra West |
7 | Terra Mystica | Lewis & Clark: The Expedition | La Granja | Kraftwagen | The Castles of Burgundy: The Card Game | Ex Libris | Underwater Cities | Clank!: Legacy – Acquisitions Incorporated |
8 | Il Vecchio | Legacy: The Testament of Duke de Crecy | Roll for the Galaxy | Grand Austria Hotel | Covert | Heaven & Ale | Architects of the West Kingdom | Nova Luna |
9 | The Great Zimbabwe | Rococo | Praetor | The Voyages of Marco Polo | Explorers of the North Sea | Riverboat | Coin & Crown | Circadians: First Light |
10 | Space Cadets | City of Remnants | Subdivision | The Pursuit of Happiness | Great Western Trail | Spirit Island | Cosmic Run: Regeneration | Ragusa |
11 | The Palaces of Carrara | Bremerhaven | Istanbul | Raiders of the North Sea | Arkham Horror: The Card Game | Calimala | Pandemic: Fall of Rome | Herbaceous Sprouts |
12 | Snowdonia | Asante | Imperial Settlers | Automania | Quadropolis | Santa Maria | Concordia Venus | Hellenica: Story of Greece |
13 | CO₂ | Euphoria: Build a Better Dystopia | Patchwork | OctoDice | Agricola: Family Edition | Valletta | Coimbra | Marco Polo II: In the Service of the Khan |
14 | Tzolk'in: The Mayan Calendar | Cinque Terre | Port Royal | Arboretum | Citadels | Caverna: Cave vs Cave | AuZtralia | Masters of Renaissance: Lorenzo il Magnifico – The Card Game |
15 | Milestones | Bora Bora | Onirim (Second Edition) | Queen's Architect | Aeon's End | Kitchen Rush | Passing Through Petra | Queenz: To Bee or Not to Bee |
16 | Clash of Cultures | Sail to India | La Isla | Through the Ages: A New Story of Civilization | Black Orchestra | Dinosaur Island | Crown of Emara | Coloma |
17 | Targi | Relic Runners | Akrotiri | Copper Country | Honshū | Coal Country | Founders of Gloomhaven | The Isle of Cats |
18 | Ginkgopolis | Spyrium | Grog Island | Discoveries: The Journals of Lewis & Clark | Cottage Garden | In the Year of the Dragon: 10th Anniversary | Dice Settlers | Tiny Towns |
19 | Le Havre: The Inland Port | The Witches: A Discworld Game | Valley of the Kings | Isle of Skye: From Chieftain to King | Star Trek: Frontiers | Notre Dame: 10th Anniversary | Grasse | Century: A New World |
20 | Love Letter | Gear & Piston | Power Grid Deluxe: Europe/North America | FUSE | The Colonists | Charterstone | New Frontiers | Islands in the Mist |
21 | Tokaido | Rogue Agent | The Golden Ages | Lanterns: The Harvest Festival | Martians: A Story of Civilization | Clans of Caledonia | Duelosaur Island | Egizia: Shifting Sands |
22 | Among the Stars | Nauticus | Panamax | Empires: Age of Discovery | La Granja: No Siesta | Lisboa | Lords of Hellas | Revolution of 1828 |
23 | Archipelago | Patchistory | Pandemic: The Cure | Valley of the Kings: Afterlife | Clank!: A Deck-Building Adventure | Montana | Tales of the Northlands: The Sagas of Noggin the Nog | Yukon Airways |
24 | Antike Duellum | Citrus | Kanban: Driver's Edition | 7 Wonders Duel | Tiny Epic Western | Fast Forward: FLEE | Azul: Stained Glass of Sintra | Barrage |
25 | Swordfish | Bruxelles 1893 | Castles of Mad King Ludwig | Hangtown | Lorenzo il Magnifico | Too Many Bones | Rise to Nobility | Paladins of the West Kingdom |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
rahdo | owned | validation | GLM | roc_auc | 0.862 |
rahdo | owned | validation | Decision Tree | roc_auc | 0.784 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 318983 | Faiyum | 0.987 | yes |
2020 | 300322 | Hallertau | 0.987 | yes |
2020 | 304420 | Bonfire | 0.983 | yes |
2020 | 184267 | On Mars | 0.981 | no |
2020 | 300327 | The Castles of Tuscany | 0.967 | yes |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.925 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.919 | yes |
2020 | 301880 | Raiders of Scythia | 0.906 | yes |
2020 | 308765 | Praga Caput Regni | 0.905 | yes |
2020 | 306040 | Merv: The Heart of the Silk Road | 0.886 | yes |
2020 | 296100 | Rococo: Deluxe Edition | 0.877 | yes |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.870 | yes |
2020 | 310442 | Feierabend | 0.868 | yes |
2020 | 293556 | Gloomy Graves | 0.853 | no |
2020 | 284742 | Honey Buzz | 0.852 | no |
2020 | 269810 | Nevada City | 0.846 | yes |
2020 | 301716 | Glasgow | 0.838 | yes |
2020 | 267009 | Rome & Roll | 0.838 | no |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.838 | no |
2020 | 312804 | Pendulum | 0.829 | yes |
2020 | 316412 | The LOOP | 0.822 | yes |
2020 | 313698 | Monster Expedition | 0.794 | no |
2020 | 302310 | Nanaki | 0.784 | no |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.757 | no |
2020 | 298065 | Santa Monica | 0.727 | yes |
2020 | 291508 | Tiny Epic Dinosaurs | 0.707 | no |
2020 | 298371 | Wild Space | 0.704 | no |
2020 | 311193 | Anno 1800 | 0.697 | yes |
2020 | 295486 | My City | 0.697 | yes |
2020 | 296626 | Sonora | 0.689 | yes |
2020 | 312484 | Lost Ruins of Arnak | 0.686 | yes |
2020 | 282954 | Paris | 0.683 | yes |
2020 | 283155 | Calico | 0.678 | yes |
2020 | 320819 | Dinner in Paris | 0.673 | no |
2020 | 317985 | Beyond the Sun | 0.665 | yes |
2020 | 316377 | 7 Wonders (Second Edition) | 0.659 | yes |
2020 | 293678 | Stellar | 0.657 | yes |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.654 | no |
2020 | 300001 | Renature | 0.645 | yes |
2020 | 279537 | The Search for Planet X | 0.639 | no |
2020 | 309000 | Blue Skies | 0.634 | no |
2020 | 325555 | Cantaloop: Book 1 – Breaking into Prison | 0.629 | no |
2020 | 281466 | Yedo: Deluxe Master Set | 0.626 | no |
2020 | 284217 | Rush M.D. | 0.625 | yes |
2020 | 284378 | Kanban EV | 0.609 | no |
2020 | 317105 | Tiny Epic Galaxies BLAST OFF! | 0.607 | no |
2020 | 307844 | Atheneum: Mystic Library | 0.603 | yes |
2020 | 282922 | Windward | 0.600 | no |
2020 | 313349 | Indus 2500 BCE | 0.593 | yes |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.582 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2021 | 343905 | Boonlake | 0.998 | yes |
2 | 2022 | 314582 | Amsterdam | 0.993 | no |
3 | 2022 | 314580 | Hamburg | 0.990 | no |
4 | 2022 | 346645 | New York City | 0.988 | no |
5 | 2022 | 341945 | La Granja: Deluxe Master Set | 0.984 | no |
6 | 2021 | 342942 | Ark Nova | 0.954 | yes |
7 | 2023 | 349793 | Age of Rome | 0.952 | no |
8 | 2022 | 302892 | Frozen Frontier | 0.914 | no |
9 | 2021 | 298378 | Maharaja | 0.912 | no |
10 | 2021 | 249277 | Brazil: Imperial | 0.912 | no |
11 | 2023 | 331820 | Rolling Heights | 0.892 | no |
12 | 2021 | 304783 | Hadrian's Wall | 0.881 | yes |
13 | 2021 | 344277 | Corrosion | 0.877 | yes |
14 | 2022 | 331106 | The Witcher: Old World | 0.867 | no |
15 | 2021 | 332944 | Sobek: 2 Players | 0.865 | no |
16 | 2022 | 317511 | Tindaya | 0.859 | no |
17 | 2021 | 300523 | Biblios: Quill and Parchment | 0.848 | no |
18 | 2021 | 329593 | Settlement | 0.846 | no |
19 | 2021 | 315234 | Embarcadero | 0.841 | no |
20 | 2021 | 238799 | Messina 1347 | 0.839 | yes |
21 | 2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.838 | no |
22 | 2021 | 283387 | Rocketmen | 0.834 | no |
23 | 2021 | 339484 | Savannah Park | 0.823 | yes |
24 | 2021 | 292899 | Tribune | 0.817 | no |
25 | 2022 | 342810 | Marrakesh | 0.814 | no |
26 | 2022 | 342674 | Jiangnan: Life of Gentry | 0.813 | no |
27 | 2021 | 331787 | Tiny Epic Dungeons | 0.811 | no |
28 | 2021 | 295947 | Cascadia | 0.805 | yes |
29 | 2022 | 319348 | Magna Roma | 0.800 | no |
30 | 2022 | 305096 | Endless Winter: Paleoamericans | 0.796 | no |
31 | 2022 | 348070 | The Palaces of Carrara (Second Edition) | 0.794 | no |
32 | 2021 | 298069 | Cubitos | 0.791 | yes |
33 | 2022 | 311988 | Frostpunk: The Board Game | 0.790 | no |
34 | 2022 | 266018 | Trinidad | 0.790 | no |
35 | 2021 | 283242 | The Whatnot Cabinet | 0.785 | yes |
36 | 2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.781 | yes |
37 | 2021 | 260524 | Beyond Humanity: Colonies | 0.780 | no |
38 | 2022 | 347703 | First Rat | 0.777 | no |
39 | 2022 | 265298 | Aquanauts | 0.776 | no |
40 | 2021 | 277700 | Merchants Cove | 0.773 | no |
41 | 2022 | 324894 | Free Radicals | 0.771 | yes |
42 | 2022 | 352201 | Skull Canyon: Ski Fest | 0.766 | no |
43 | 2022 | 305462 | The Age of Atlantis | 0.763 | no |
44 | 2022 | 266064 | Trudvang Legends | 0.762 | no |
45 | 2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.755 | no |
46 | 2022 | 310873 | Carnegie | 0.751 | no |
47 | 2021 | 328286 | Mission ISS | 0.749 | no |
48 | 2021 | 289550 | Lions of Lydia | 0.743 | yes |
49 | 2021 | 281248 | Cape May | 0.739 | no |
50 | 2021 | 339789 | Welcome to the Moon | 0.734 | yes |
51 | 2022 | 350316 | Wayfarers of the South Tigris | 0.732 | no |
52 | 2021 | 341169 | Great Western Trail (Second Edition) | 0.729 | yes |
53 | 2021 | 252752 | Genotype: A Mendelian Genetics Game | 0.727 | yes |
54 | 2021 | 325698 | Juicy Fruits | 0.724 | yes |
55 | 2021 | 333553 | For the King (and Me) | 0.720 | no |
56 | 2022 | 319807 | Shogun no Katana | 0.719 | no |
57 | 2022 | 328124 | Bot Factory | 0.717 | no |
58 | 2022 | 356414 | Space Station Phoenix | 0.712 | yes |
59 | 2021 | 344768 | Mobile Markets: A Smartphone Inc. Game | 0.710 | yes |
60 | 2022 | 356033 | Libertalia: Winds of Galecrest | 0.706 | no |
61 | 2021 | 328479 | Living Forest | 0.704 | no |
62 | 2022 | 352695 | Oranienburger Kanal | 0.704 | no |
63 | 2022 | 294702 | Tenpenny Parks | 0.697 | no |
64 | 2022 | 343927 | Union Stockyards | 0.697 | no |
65 | 2021 | 339906 | The Hunger | 0.689 | yes |
66 | 2022 | 342046 | Phraya | 0.689 | no |
67 | 2022 | 322565 | Silicon Valley | 0.682 | no |
68 | 2021 | 332290 | Stardew Valley: The Board Game | 0.681 | no |
69 | 2021 | 309319 | Apogee | 0.679 | no |
70 | 2022 | 258779 | Planet Unknown | 0.675 | no |
71 | 2021 | 322195 | Kokopelli | 0.673 | yes |
72 | 2021 | 316786 | Tabannusi: Builders of Ur | 0.666 | yes |
73 | 2022 | 359764 | Shake That City | 0.656 | no |
74 | 2021 | 292375 | The Great Wall | 0.652 | yes |
75 | 2021 | 332386 | Brew | 0.645 | yes |
76 | 2021 | 299684 | Khôra: Rise of an Empire | 0.641 | yes |
77 | 2021 | 324242 | Sheepy Time | 0.638 | yes |
78 | 2021 | 298383 | Golem | 0.632 | yes |
79 | 2021 | 341048 | Free Ride | 0.631 | no |
80 | 2022 | 323707 | MOB: Big Apple | 0.631 | no |
81 | 2021 | 302510 | Mining Colony | 0.629 | no |
82 | 2021 | 310198 | Escape: Roll & Write | 0.627 | yes |
83 | 2023 | 312682 | Silver Coin: Age of Monster Hunters | 0.621 | no |
84 | 2022 | 326945 | Castles of Mad King Ludwig: Collector's Edition | 0.618 | no |
85 | 2021 | 297563 | Faraway Valley | 0.617 | no |
86 | 2021 | 317457 | Dinosaur World | 0.608 | yes |
87 | 2021 | 340455 | King of the Valley | 0.608 | no |
88 | 2022 | 352263 | Through Ice and Snow | 0.604 | no |
89 | 2021 | 322588 | Origins: First Builders | 0.604 | yes |
90 | 2021 | 315937 | X-Men: Mutant Insurrection | 0.602 | yes |
91 | 2022 | 345088 | Founders of Teotihuacan | 0.597 | yes |
92 | 2021 | 343526 | G.I. JOE Deck-Building Game | 0.595 | yes |
93 | 2022 | 351097 | Townies | 0.592 | no |
94 | 2021 | 306202 | Philosophia: Floating World | 0.590 | no |
95 | 2022 | 280726 | Legacies | 0.589 | no |
96 | 2022 | 344620 | Pocket Master Builder | 0.586 | no |
97 | 2021 | 322622 | Floriferous | 0.585 | yes |
98 | 2022 | 331190 | Meeples & Monsters: Kickstarter Edition | 0.571 | no |
99 | 2023 | 304510 | Pampero | 0.568 | no |
100 | 2021 | 314491 | Meadow | 0.567 | no |
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 6032377 322.2 13980205 746.7 NA 13980205 746.7
## Vcells 173019845 1320.1 558901608 4264.1 102400 1113448843 8495.0